AI-Induced Vascular Ages Are a Measurable Residual Risk for Cardiovascular Diseases in the Japanese Population

Background: Cardiovascular diseases (CVDs) remain a leading cause of morbidity and mortality, despite advances in treatment. Early detection of vascular aging is critical, as preclinical atherosclerosis often remains undiagnosed. AI-determined vascular age, originally developed using carotid-femoral pulse wave velocity (cf-PWV), may help to identify individuals at elevated risk. This study aimed to evaluate the clinical utility of an alternative AI-determined vascular age model based on the arterial velocity pulse index (AVI) and arterial pressure volume index (API) in a Japanese hospital-based cohort. Methods: This retrospective, exploratory study analyzed electronic health records of 408 patients from Yokohama City University Hospital. This study was approved by the Clinical Research Ethics Committee (approval numbers: B180300040, F240500007), and patient consent was obtained through an opt-out process. AI-determined vascular age was estimated using a Generalized Additive Model (GAM) with backward stepwise regression, substituting cf-PWV with AVI and API. Correlations with chronological age were assessed, and comparisons of cardiovascular and renal function markers were performed across age-stratified groups. Results: AI-determined vascular age showed a strong correlation with chronological age (p < 0.05). Significant differences were observed in cardiac diastolic function parameters, B-type natriuretic peptide (BNP), and estimated glomerular filtration rate (eGFR) between the highest and lowest quintiles of AI-determined vascular age. Conclusions: AI-determined vascular age using AVI and API appears to be a feasible surrogate for cf-PWV in clinical settings. This index may aid in stratifying vascular aging and identifying individuals who could benefit from early cardiovascular risk management.